Welcome TurkHackTeam Members,
If you read my other posts, you know that I'm interested in python. So that most of my programming tutorials are about python. And today I will write the best libraries to develop AI in python.
So let's begin.
Keras
I want to start with Keras. Written in Python programming language, Keras is a neural network open-source library. It can run on top of TensorFlow and other libraries like Theano and PlaidML, which gives it an upper hand. Since the library is mainly designed for deep neural networks, it is modular, extensible, and user-friendly making it perfect for beginners. It seamlessly works with other building blocks of neural networks like objectives, layers, optimisers, and activation functions. It can run on both GPU as well as CPU and it also allows for fast prototyping. I have not used this one yet, but I will perhaps soon.
PyTorch
Well, in fact I use PyTorch even in my regular projects. So what's PyTorch? PyTorch is a deep learning library which is used by applications like natural language processing and computer vision. Developed by Facebook, it is open-source, free, and released under the modified BSD license. The Python library for AI and ML is based on Torch library, and thats how it gets its name. PyTorch can easily be integrated with other Python data science stacks and it also helps developers in performing computations on tensors.
Theano
Some of my friends use this library often. Theano is a Python library which is majorly used for fast numerical computation and it can run on both GPU and CPU. Since it is built on top of NumPy, Theano is pretty tightly integrated with NumPy and it has a similar interface as well. The library is perfect for manipulating and evaluating mathematical expressions as well as matrix calculations. With Theano, you can perform data-intensive computations that are up to 140x faster. It also has built-in tools for validation and unit testing, making it easier to avoıd any problems or bugs.
Scikit-learn
Scikit is used more often than the others. Scikit-learn is a free machine learning library based on Python learning language which features a wide range of unsupervised and supervised learning algorithms. It is built on two of the basic Python libraries SciPy and NumPy. It has numerous classification, clustering, and regression algorithms available in it like random forests, k-means, and gradient boosting. The library can also help with dimensionality reduction, preprocessing, and model selection. Developers mainly deploy the Scikit-learn library for data mining and analysis.
TensorFlow
TensorFlow is an open-source and free software library mainly used for differential programming. It is a math library that is used by machine learning applications and neural networks. While it was definitely not the first Python library in the world, it is the most popular library for machine learning because of its wide features and ease of use. TensorFlow supports a wide variety of toolkits for creating models of different levels of abstraction.
Tensorflow has even an official source website: https://www.tensorflow.org/
You can learn this library easier.
Pandas
Pandas is mostly used for data science, however it is used for AI as well. Pandas is a Python library which is consistently becoming more popular. It helps developers build high-level data structures that are intuitive and seamless. There are inbuilt methods available in Pandas for data filtering, combining, and grouping. It is highly stable and it can be used to perform time-series analysis. With Pandas, you can easily fetch and manipulate data from different sources like Excel, CSV, and JSON file. There are two main data structures available in Pandas Series (One dimensional) and Data Frame (Two Dimensional).
Thanks for reading. Have a good day.
If you read my other posts, you know that I'm interested in python. So that most of my programming tutorials are about python. And today I will write the best libraries to develop AI in python.
So let's begin.
Keras
I want to start with Keras. Written in Python programming language, Keras is a neural network open-source library. It can run on top of TensorFlow and other libraries like Theano and PlaidML, which gives it an upper hand. Since the library is mainly designed for deep neural networks, it is modular, extensible, and user-friendly making it perfect for beginners. It seamlessly works with other building blocks of neural networks like objectives, layers, optimisers, and activation functions. It can run on both GPU as well as CPU and it also allows for fast prototyping. I have not used this one yet, but I will perhaps soon.
PyTorch
Well, in fact I use PyTorch even in my regular projects. So what's PyTorch? PyTorch is a deep learning library which is used by applications like natural language processing and computer vision. Developed by Facebook, it is open-source, free, and released under the modified BSD license. The Python library for AI and ML is based on Torch library, and thats how it gets its name. PyTorch can easily be integrated with other Python data science stacks and it also helps developers in performing computations on tensors.
Theano
Some of my friends use this library often. Theano is a Python library which is majorly used for fast numerical computation and it can run on both GPU and CPU. Since it is built on top of NumPy, Theano is pretty tightly integrated with NumPy and it has a similar interface as well. The library is perfect for manipulating and evaluating mathematical expressions as well as matrix calculations. With Theano, you can perform data-intensive computations that are up to 140x faster. It also has built-in tools for validation and unit testing, making it easier to avoıd any problems or bugs.
Scikit-learn
Scikit is used more often than the others. Scikit-learn is a free machine learning library based on Python learning language which features a wide range of unsupervised and supervised learning algorithms. It is built on two of the basic Python libraries SciPy and NumPy. It has numerous classification, clustering, and regression algorithms available in it like random forests, k-means, and gradient boosting. The library can also help with dimensionality reduction, preprocessing, and model selection. Developers mainly deploy the Scikit-learn library for data mining and analysis.
TensorFlow
TensorFlow is an open-source and free software library mainly used for differential programming. It is a math library that is used by machine learning applications and neural networks. While it was definitely not the first Python library in the world, it is the most popular library for machine learning because of its wide features and ease of use. TensorFlow supports a wide variety of toolkits for creating models of different levels of abstraction.
Tensorflow has even an official source website: https://www.tensorflow.org/
You can learn this library easier.
Pandas
Pandas is mostly used for data science, however it is used for AI as well. Pandas is a Python library which is consistently becoming more popular. It helps developers build high-level data structures that are intuitive and seamless. There are inbuilt methods available in Pandas for data filtering, combining, and grouping. It is highly stable and it can be used to perform time-series analysis. With Pandas, you can easily fetch and manipulate data from different sources like Excel, CSV, and JSON file. There are two main data structures available in Pandas Series (One dimensional) and Data Frame (Two Dimensional).
Thanks for reading. Have a good day.
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